Split a dataset created by Tensorflow dataset API in to Train and Test?

Question

Does anyone know how to split a dataset created by the dataset API (tf.data.Dataset) in Tensorflow into Test and Train?

Answer 1

You may use Dataset.take() and Dataset.skip():

train_size = int(0.7 * DATASET_SIZE)
val_size = int(0.15 * DATASET_SIZE)
test_size = int(0.15 * DATASET_SIZE)

full_dataset = tf.data.TFRecordDataset(FLAGS.input_file)
full_dataset = full_dataset.shuffle()
train_dataset = full_dataset.take(train_size)
test_dataset = full_dataset.skip(train_size)
val_dataset = test_dataset.skip(val_size)
test_dataset = test_dataset.take(test_size)

For more generality, I gave an example using a 70/15/15 train/val/test split but if you don't need a test or a val set, just ignore the last 2 lines.

Take:

Creates a Dataset with at most count elements from this dataset.

Skip:

Creates a Dataset that skips count elements from this dataset.

You may also want to look into Dataset.shard():

Creates a Dataset that includes only 1/num_shards of this dataset.

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Disclaimer I stumbled upon this question after answering this one so I thought I'd spread the love

Answer 2

Can't comment, but above answer has overlap and is incorrect. Set BUFFER_SIZE to DATASET_SIZE for perfect shuffle. Try different sized val/test size to verify. Answer should be:

DATASET_SIZE = tf.data.experimental.cardinality(full_dataset).numpy()
train_size = int(0.7 * DATASET_SIZE)
val_size = int(0.15 * DATASET_SIZE)
test_size = int(0.15 * DATASET_SIZE)

full_dataset = full_dataset.shuffle(BUFFER_SIZE)
train_dataset = full_dataset.take(train_size)
test_dataset = full_dataset.skip(train_size)
val_dataset = test_dataset.take(val_size)
test_dataset = test_dataset.skip(val_size)

Answer 3

@ted's answer will cause some overlap. Try this.

train_ds_size = int(0.64 * full_ds_size)
valid_ds_size = int(0.16 * full_ds_size)

train_ds = full_ds.take(train_ds_size)
remaining = full_ds.skip(train_ds_size)  
valid_ds = remaining.take(valid_ds_size)
test_ds = remaining.skip(valid_ds_size)

use code below to test.

tf.enable_eager_execution()

dataset = tf.data.Dataset.range(100)

train_size = 20
valid_size = 30
test_size = 50

train = dataset.take(train_size)
remaining = dataset.skip(train_size)
valid = remaining.take(valid_size)
test = remaining.skip(valid_size)

for i in train:
    print(i)

for i in valid:
    print(i)

for i in test:
    print(i)

Answer 4

Now Tensorflow doesn't contain any tools for that.
You could use sklearn.model_selection.train_test_split to generate train/eval/test dataset, then create tf.data.Dataset respectively.

Answer 5

Assuming you have all_dataset variable of tf.data.Dataset type:

test_dataset = all_dataset.take(1000) 
train_dataset = all_dataset.skip(1000)

Test dataset now has first 1000 elements and the rest goes for training.

Answer 6

You can use shard:

dataset = dataset.shuffle()  # optional
trainset = dataset.shard(2, 0)
testset = dataset.shard(2, 1)

See: https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shard